The goal of the proposed work is to systematically identify consequences of stochastic cell- to-cell variation in protein expression levels, which is pervasive for many genes in all living systems. In order to do this we will measure the distribution of expression levels among cells for a panel of proteins in budding yeast, the rate and modularity of stochastic switching between alternative expression states, and natural genetic variation in cell-to-cell variation distributions. It has been hypothesized that dynamic environments play a driving role in the evolution of stochastic expression variation. Using budding yeast as a study system, this hypothesis will be systematically tested by measuring the extent that the fitness consequences of stochastic expression differ across stress treatments. This survey will also systematically uncover evidence for bet hedging, a strategy whereby stochastic expression variation contributes to the differential survival within the population following a shift in the environment. A fundamental parameter of stochastic expression variation is the rate at which cells switch among alternative regulatory states, called the expression state switching rate (ESSR). For most proteins, the ESSR creates noise in the form of a continuous distribution of expression across cells in a population. The hypothesis that genes exhibit differences in ESSRs will be systematically tested. Although cell-to-cell variation has been previously measured one-protein at a time, it is not known whether such stochasticity reflects larger scale modular remodeling of expression across the genome, for example as part of the environmental stress response, and this study will provide the first empirical test of the modularity of stochastic expression variation. The functional importance of bet hedging, cellular memory, and epigenetic inheritance suggests they are important mechanisms for adaptation to new environments and ecologies. However, the extent that stochastic protein expression and ESSR evolve are variable among genetic backgrounds has not been systematically tested. This will be tested for key genes across a panel of genetic backgrounds. Measurement of cell-specific protein expression levels will be made using flow cytometry. Expression state switching rate for these proteins and its effect on cellular growth will be measured by time-lapse microscopy and by using flow cytometry to sort cells into separate populations based on the differential level of expression, and then measuring both growth rates and changes in protein levels in each sub-population over a time. Whether cells with stochastic differences in a particular protein also exhibit expression differences in other genes involved in the same regulatory modules will be tested by applying transcriptome sequencing to cell populations sorted by expression level. Finally, genetic variation in stochastic expression and its consequences among each of these proteins will be assayed by creating GFP-fusion proteins across genetically and ecologically diverse strains of budding yeast, including pathogenic, agricultural, and wild strains. The outcome will be identification of proteins that show genetic variation in stochastic expression in one or more of these environments, which cannot be explained by simple changes in protein abundance.